diff --git a/python/paddle/fluid/tests/unittests/op_test.py b/python/paddle/fluid/tests/unittests/op_test.py
index 2d678db4dfcb48ddefb3170ad4285112b1ba8391..0e5202209e494136b3599e714e7cf80a51b1a04b 100644
--- a/python/paddle/fluid/tests/unittests/op_test.py
+++ b/python/paddle/fluid/tests/unittests/op_test.py
@@ -713,44 +713,76 @@ class OpTest(unittest.TestCase):
             def is_empty(a):
                 return isinstance(a, Empty)
 
-            def get_default(idx, all_params_number, defaults):
-                related_idx = idx - all_params_number + len(defaults)
-                assert related_idx >= 0, "%d-th arguments don't have default value" % idx
-                return defaults[related_idx]
-
-            def filter_by_name(x):
-                names = set(['name', 'out', 'output'])
-                if isinstance(x, list): return [i for i in x if i not in names]
-                if isinstance(x, dict):
-                    return {k: v for k, v in x.items() if k not in names}
-                assert False, "Only support list or dict."
+            def get_default(idx, defaults):
+                assert not isinstance(
+                    defaults[idx], Empty
+                ), "%d-th params of python api don't have default value." % idx
+                return defaults[idx]
 
             def to_defaults_list(params, defaults):
                 return [defaults[p] for p in params if p in defaults]
 
-            # NOTE(xiongkun): why don't use input arguments dicts ? 
-            # Because we don't know the python api name of each arguments.
-            # using parse_arg_and_kwargs, we can get the all api information we need.
-            api_params, api_defaults = [
-                filter_by_name(item) for item in parse_arg_and_kwargs(api)
-            ]
+            def parse_attri_value(name, op_inputs, op_attrs):
+                """ parse true value from inputs and attrs, if there is no name passed by OpTest, return Empty
+                    1. if the name in op_attrs, use the op_attrs[name]
+                    2. if the name in op_inputs, convert the op_inputs to [type of default value]
+                    3. if the name not in op_attrs ans op_inputs, return Empty. (this will use the default value from python api)
+                """
+                if name in op_proto_attrs:
+                    return op_proto_attrs[name]
+                elif name in op_inputs:
+                    assert op_inputs[name].__len__(
+                    ) == 1, "currently don't support multi-input in attribute."
+                    # why don't use numpy().item() : if the Tensor is float64, we will change it to python.float32, where we loss accuracy: [allclose_op]
+                    # why we reconstruct a tensor: because we want the tensor in cpu. 
+                    return paddle.to_tensor(
+                        op_inputs[name][0].numpy(), place='cpu')
+                else:
+                    return Empty()
+
+            # NOTE(xiongkun): the logic of constructing parameters: 
+            # for example:  
+            #    python api: cumprod(x, dim, dtype=None, name=None) 
+            #    kernel sig: [["x"], ["dim"], ["out"]]"
+            #
+            # we will construct a lot of list with the same length : len == len(api_params), here is 4
+            #    api_params = ["x", "dim", "dtype", "name"]
+            #    api_defaults = [Empty, Empty, None, None]; empty means no defaults.
+            #    inputs_and_attrs = ["x", "dim"] , the length may shorter or longer than api_params
+            #    input_arguments = [RealValue in self.inputs and self.attrs]
+            # then ,we will loop for the api_params, construct a result list: 
+            #    if the name in ['name', 'dtype', 'out', 'output'], we will use the default value
+            #    else, we will consume a input_arguments. (because the name is not corresponding, so we only use the order)
+
+            api_params, api_defaults = parse_arg_and_kwargs(api)
             api_defaults = to_defaults_list(api_params, api_defaults)
+            api_defaults = [
+                Empty() for i in range(len(api_params) - len(api_defaults))
+            ] + api_defaults
+            assert len(api_defaults) == len(
+                api_params), "Error happens. contack xiongkun03 to solve."
             inputs_sig, attrs_sig, outputs_sig = kernel_sig
             inputs_and_attrs = inputs_sig + attrs_sig
-            assert (
-                len(api_params) == len(inputs_and_attrs)
-            ), "inputs and attrs length must equals to python api length. (May be output is in argument list?)"
             input_arguments = [op_proto_ins[name] for name in inputs_sig] + [
-                op_proto_attrs[name] if name in op_proto_attrs else Empty()
+                parse_attri_value(name, op_proto_ins, op_proto_attrs)
                 for name in attrs_sig
             ]
             results = []
-            for idx, arg in enumerate(input_arguments):
-                if is_empty(arg):
-                    results.append(
-                        get_default(idx, len(input_arguments), api_defaults))
+            api_ignore_param_list = set(['name', 'dtype', 'out', 'output'])
+            idx_of_op_proto_arguments = 0
+            for idx, arg_name in enumerate(api_params):
+                if arg_name in api_ignore_param_list:
+                    results.append(get_default(idx, api_defaults))
                 else:
-                    results.append(arg)
+                    assert idx_of_op_proto_arguments < len(
+                        input_arguments), "Assert False."
+                    tmp = input_arguments[idx_of_op_proto_arguments]
+                    idx_of_op_proto_arguments += 1
+                    if isinstance(tmp, Empty):
+                        results.append(get_default(idx, api_defaults))
+                    else:
+                        results.append(tmp)
+            assert len(results) == len(api_params)
             return results
 
         def construct_output_dict_by_kernel_sig(ret_tuple, output_sig):